1. Field of the Invention
This invention relates generally to the field of radar signature analysis. More particularly, it relates to information theoretic methods for assessing radar signature databases for use in training a radar target recognition decision algorithm and for identifying real-time target measurements using automatic target recognition.
2. Description of the Related Art
The ability to make radar signature databases portable for use within similar sensor systems may be critical to the affordability of future airborne signature exploitation systems. The capability to hybridize measured and synthetic signature database components may maximize the impact of the investment required to build complex radar signature databases. Radar target scattering mechanisms may be modeled and the signature signal model analyzed as a random process to enable portability and hybridization. Modal mutual information may be developed as a measure of similarity to compare measured signature data to modeled synthetic data. The inherent qualities of mutual information to be used in the context of the automatic target recognition problem may be demonstrated using synthetic signature sets comprised of both “similar targets” and “dissimilar targets.”
Signature exploitation systems are of ever-increasing importance both in air-to-air and air-to-ground sensor systems. Successful implementation of these systems often requires a robust and integrated signature database for training exploitation algorithms. Signature training databases should represent the radar measured signature process across a wide range of target articulations and configurations, as well as under many operating conditions including clutter, obscuration, and other sources of RF interference. It is also useful to have signature databases that are portable for use in similar sensor applications. For example, it is desired that airborne radar systems associated with a type of aircraft be able to share a common radar signature database. Construction of a signature database based entirely on measurements is expensive and can be an impractical proposition. It is possible to construct a signature database using electromagnetic scattering codes.
However, given the complexity of typical targets including personnel carriers, tanks, aircraft, and missiles, etc., and the challenge of modeling a variety of electromagnetic scattering phenomena ranging from specular reflection to edge diffraction, smooth surface diffraction etc., computation of signatures with sufficient accuracy is a challenging task. Furthermore, it needs to be established that the computed signatures are consistent with measured signatures. The validation of the computed or surrogate sensor signature process with the measured signature process enables the expanded use of multi-source signature data for algorithm training within ongoing automatic target recognition (AIR) theory efforts, nearly all of which depend on a valid characterization of the signature scattering model for all targets of interest.
The use of high resolution radar measurements has been useful in the support of research and study of signature exploitation capability within airborne platforms. In view of the uncertainties in the aspect angle of the target, the high resolution signature may be considered to be a random vector. Given the changing geometry relative to the target within a typical radar measurement interval, the statistics associated with the high resolution random vector are often time varying. Therefore, the measured high resolution signature of the target at a given time “t” is a realization of a multidimensional random process (time varying random vector). If the target statistics are assumed to be stationary (constant with time), the sample signatures associated with this random vector correspond to a range of aspect angles in a small window about this reference.
The problem of validation is quite different from the design of target recognition algorithms. In the case of automatic target recognition algorithms, a signature measured under field conditions (which may be considered to be a sample realization of a random process) is compared to the signature random process corresponding to the different target classes of interest comprising a database. Unlike the automatic target recognition problem, the database similarity problem (validation) involves the comparison of two random signature processes.